Observation window

Abstract

A terminal device 110 receives from a network device 120 a Channel State Information (CSI) report configuration for artificial intelligence / machine learning (AI / ML) based beam management (BM) for temp

Description

FIELD

[0001] Example embodiments of the present disclosure generally relate to the field of communication, and in particular, to devices, methods, apparatuses, and non-transitory computer readable mediums associated with an observation window, for example, an observation window configuration for artificial intelligence / machine learning (AI / ML)-based management (BM), especially for temporal domain beam prediction. BACKGROUND

[0002] A communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network. A mobile or wireless communication network is one example of a communication network. Such communication networks operate in accordance with standards, such as those promulgated by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of such standards include the so-called 5G (5th Generation) standard or other standards promulgated by 3GPP. SUMMARY

[0003] In general, example embodiments of the present disclosure provide a solution for an observation window, for example, an observation window observation window configuration for AUML-based temporal domain beam prediction.

[0004] In a first aspect, there is provided a terminal device. The terminal device comprises at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: receive a channel state information (CSI) report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and transmit a CSI report based on the CSI report configuration.

[0005] In a second aspect, there is provided a network device. The network device comprises at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to: transmit, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and receive, from the terminal device, a CSI report based on the CSI report configuration.

[0006] In a third aspect, there is provided a method. The method comprises: receiving, from a network device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and transmitting, to the network device, a CSI report based on the CSI report configuration.

[0007] In a fourth aspect, there is provided a method. The method comprises: transmitting, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and receiving, from the terminal device, a CSI report based on the CSI report configuration.

[0008] In a fifth aspect, there is provided an apparatus. The apparatus comprises: means for receiving, from a network device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and means for transmitting, to the network device, a CSI report based on the CSI report configuration.

[0009] In a sixth aspect, there is provided an apparatus. The apparatus comprises: means for transmitting, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and means for receiving, from the terminal device, a CSI report based on the CSI report configuration.

[0010] In a seventh aspect, there is provided a non-transitory computer-readable storage medium comprising program instructions. The program instructions, when executed by an apparatus, cause the apparatus to perform: receive, from a network device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and transmit, to the network device, a CSI report based on the CSI report configuration.

[0011] In an eighth aspect, there is provided a non-transitory computer-readable storage medium comprising program instructions. The program instructions, when executed by an apparatus, cause the apparatus to perform: transmit, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and receive, from the terminal device, a CSI report based on the CSI report configuration.

[0012] In a ninth aspect, there is provided a computer program, or computer readable medium, comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: receive a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and transmit a CSI report based on the CSI report configuration.

[0013] In a tenth aspect, there is provided a computer program, or computer readable medium, comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: transmit, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and receive, from the terminal device, a CSI report based on the CSI report configuration.

[0014] In an eleventh aspect, there is provided a terminal device. The terminal device comprises receiving circuitry configured to receive a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and transmitting circuitry configured to transmit a CSI report based on the CSI report configuration.

[0015] In a twelfth aspect, there is provided a network device. The network device comprises transmitting circuitry configured to transmit, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and receiving circuitry configured to receive, from the terminal device, a CSI report based on the CSI report configuration.

[0016] It is to be understood that the summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0018] FIG. I illustrates an example communication network in which embodiments of the present disclosure may be implemented;

[0019] FIG. 2 illustrates an example of a process flow in accordance with some example embodiments of the present disclosure;

[0020] FIG. 3 illustrates a flowchart of an example method implemented at a terminal device in accordance with some other embodiments of the present disclosure;

[0021] FIG. 4 illustrates a flowchart of an example method implemented at a network device in accordance with some other embodiments of the present disclosure;

[0022] FIG. 5 illustrates a simplified block diagram of a device that is suitable for implementing some example embodiments of the present disclosure; and

[0023] FIG. 6 illustrates a block diagram of an example of a computer-readable medium in accordance with some example embodiments of the present disclosure.

[0024] Throughout the drawings, the same or similar reference numerals represent the same or similar elements. DETAILED DESCRIPTION

[0025] Principles of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones described below.

[0026] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0027] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0028] It shall be understood that although the terms “first” and “second” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. As used herein, “at least one of the following: <a list of two or more elements>” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0030] As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example, firmware) for operation, but the software may not be present when it is not needed for operation.

[0031] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0032] As used herein, the term “network”, “communication network” or “data network” refers to a network following any suitable communication standards, such as long term evolution (LTE), LTE-advanced (LTE-A), wideband code division multiple access (WCDMA), high-speed packet access (HSPA), narrow band Internet of things (NB-IoT), wireless fidelity (Wi-Fi) and so on. Furthermore, the communications between a terminal device and a network device / element in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, 6 the fourth generation (4G), 4.5G, the future fifth generation (5G), IEEE 802.11 communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0033] As used herein, the term “network device” refers to a node in a communication network via which a terminal device receives services (e.g., positioning services) therefrom. The network device may refer to a core network device or access network device, such as base station (BS) or an access point (AP) or a transmission and reception point (TRP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a NR NB (also referred to as a gNB), a remote radio unit (RRU), a radio header (RH), a remote radio head (RRH), a WiFi device, a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. In the following description, the terms “network device”, “AP device”, “ AP” and “access point” may be used interchangeably.

[0034] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), a station (STA) or station device, or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (for example, remote surgery), an industrial device and applications (for example, a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. In the following description, the terms “station”, “station device”, “STA”, “terminal device”, “communication device”, “terminal”, “user equipment” and “UE may be used interchangeably.

[0035] It has been considered defining Radio Resource Management (RRM) requirements for Life Cycle Management (LCM) procedures based on the specific use cases (such as beam management and positioning, etc.). The candidates may comprise but not limit model identification, selection, activation, deactivation, switching, fallback to non-AI operation, performance monitoring.

[0036] Signailing and protocol aspects of LCM enable functionality and model selection, activation, deactivation, switching, fallback, etc. Necessary signalling / mechanism(s) for LCM to may facilitate model training, inference, performance monitoring, data collection for both UE-sided and NW-sided models. For example, specify necessary signalling / mechanism(s) are able to facilitate LCM operations specific to the BM use case.

[0037] Beam prediction is one critical aspect of beam management. In recent years, AI has gained significant attention and extensive researches across various fields. With advanced AI / ML-driven solutions, the performance of beam prediction can be improved, thereby facilitating the selection of appropriate beam(s) with accuracy and efficiency.

[0038] The incorporation of AI / ML technologies facilitates the enhancement of beam prediction. AI / ML-based beam prediction involves prediction on both spatial domain and temporal domain. The scope of the spatial domain beam prediction (also referred to as BM-Casel) is to predict the optimal Tx / Rx beams in different spatial locations, and the temporal domain beam prediction (also referred to as BM-Case2) aims to predict the most likely used beam for next time instants.

[0039] For Example, DL Tx beam prediction for both UE-sided model and NW-sided model comprises spatial domain DL Tx beam prediction and temporal domain DL Tx beam prediction. The spatial domain DL Tx beam prediction for Set A of beams is based on measurement results of Set B of beams, and the temporal domain DL Tx beam prediction for Set A of beams is based on the historic measurement results of Set B of beams.

[0040] In current 5G system, beam measurements, e.g., layer 1-reference signal received power (Ll-RSRP) measurements and reporting, are used by the networks. The measurements of the Set B of beams are used as input to the ML model, and the ML model outputs, based on the input from the Set B of beams, the best K beams (beam indices and / or Ll-RSRP values) from Set A of beams. For Ll-RSRP measurement and reporting based AI / ML beam prediction, the UE would need to report the measured Ll-RSRP for both BM-Casel (spatial domain beam prediction) and BM-Case2 (temporal domain beam prediction) based on measurement delay condition. For BM-Case2, the history of measurements and reporting in multiple time instances need to be reported as an observation window.

[0041] Regarding BM-Case2, the current solutions for measurement and reporting related to CSI-RS-based Ll-RSRP reporting are not compatible with AEML-based beam prediction for temporal domain. Furthermore, without the measurement for the observation window, it is challenging to identify the required history of measurements and reporting. Given these challenges, a novel approach for enhancement is essential.

[0042] Embodiments of the present disclosure provide a solution for observation window configuration for AEML-based temporal domain beam prediction. Detailed description of the signaling framework with procedures and exchanged signaling to enable AI / ML-based temporal domain beam prediction and to indicate a number of time instances for measurements and report the measurements based on the measurement delay condition are depicted.

[0043] For illustrative purposes, principles and example embodiments of the present disclosure will be described below with reference to FIG. 1 to FIG. 6. However, it is to be noted that these embodiments are given to enable the skilled in the art to understand inventive concepts of the present disclosure and implement the solution as proposed herein, and not intended to limit scope of the present application in any way.

[0044] FIG. 1 illustrates an example of an application scenario 100 in which some example embodiments of the present disclosure may be implemented. The application scenario 100, which is a part of a communication network, includes terminal devices and network devices.

[0045] In the descriptions of the example embodiments of the present disclosure, the network environment 100 may also be referred to as a communication system 100 (for example, a portion of a communication network). For illustrative purposes only, various aspects of example embodiments will be described in the context of one or more terminal devices and network devices that communicate with one another. It should be appreciated, however, that the description herein may be applicable to other types of apparatus or other similar apparatuses that are referenced using other terminology.

[0046] The communication system 100 may comprise a terminal device 110 (hereinafter may also be referred to as UE 110). The communication network 100 may further comprise a network device 120 (hereinafter may also be referred to as gNB 120). The network device 120 may manage a cell 101. The terminal device 110 and the network device 120 may communicate data and control information to each other in the coverage of the cell 101. A link from the network device 120 to the terminal device 110 is referred to as a downlink (DL), while a link from the terminal device 110 to the network device 120 is referred to as an uplink (UL).

[0047] Communications in the communication system 100 may be implemented according to any proper wireless or wired communication protocol(s), comprising, but not limited to, cellular communication protocols and core network communication protocols of the fourth generation (4G) and the fifth generation (5G) and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0048] It is to be understood that the number of devices and their connection relationships and types shown in FIG. 1 are for illustrative purposes without suggesting any limitation. The communication system 100 may comprise any suitable number of devices adapted for implementing embodiments of the present disclosure.

[0049] FIG. 2A illustrates an example of a process flow 200A of observation window configuration for delay-related measurements and reporting based on AI / ML-based temporal domain beam prediction in accordance with some example embodiments of the present disclosure. For ease of understanding, the process flow 200A will be described with reference to FIG. 1. It would be appreciated that although the process flow 200A has been described referring to the application scenario 100 of FIG. 1, this process flow 200A may be likewise applied to other similar communication scenarios.

[0050] The network device 120 transmits (201), to the terminal device 110, a CSI report configuration (202) for AI / ML-BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window 10 and reporting the measurements based on measurement delay condition. The parameter may be a higher layer parameter. Configuration of the observation window for AI / ML-based temporal domain beam prediction (i.e., BM-Case2) on UE-sided model may be indicated by the CSI report configuration transmitted to the terminal device 110. The observation window may indicate the historic measurement results of Set B of beams.

[0051] The terminal device 110 receives (203) the CSI report configuration (202) for AI / ML-based BM for temporal domain beam prediction, including the parameter indicating the number of time instances for the measurements in the observation window and reporting the measurements based on the measurement delay condition. The terminal device 110 transmits (205) a CSI report (206) to the network device 120. The CSI report may be generated based on the received CSI report configuration from network side, and be transmitted to the network side for responding the network device 120. The network device 120 receives (207) the CSI report (206).

[0052] FIG. 2B illustrates an example of a process flow 200B of the performing process 200B of the delay related operations in accordance with some example embodiments of the present disclosure. For ease of understanding, the process flow 200B will be described with reference to FIG. 1. It would be appreciated that although the process flow 200B has been described referring to the application scenario 100 of FIG. 1, this process flow 200B may be likewise applied to other similar communication scenarios.

[0053] The network device 120 transmits (207), to the terminal device 110, an activation command (208) for temporal domain beam prediction functionality (e.g., AI / ML-based temporal domain beam prediction). In some embodiments, the activation command could be the CSI report configuration. The CSI report configuration is associated to time requirement defined with measurement period. The corresponding delay related operations shall be performed no later than the time requirement. The terminal device 110 receives (209) the activation command for temporal domain beam prediction functionality. The activation command could be the CSI report configuration.

[0054] Within the defined time requirement, the UE shall finish the corresponding delay related operations, for example, measurements during observation window, use of the AI / ML model for obtaining the predictions, or report of the prediction, etc. The terminal device 110 transmits (210) the report of the prediction (211) to the network device 120. The report of the prediction may be corresponding to the obtained predictions by using the AI / ML model. The network device 120 receives (212) the report of the prediction (211).

[0055] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may be the timeRestrictionForChannelMeasurements parameter (hereinafter may also be referred to as the first higher layer parameter) for configuring time domain restriction for channel measurements, or a new higher layer parameter (hereinafter may also be referred to as the second higher layer parameter) for configuring the number of time instances of the measurements used by the terminal device, etc. The number of time instances of the measurements included in the observation window may be also called the length of the observation window.

[0056] In one example, when the timeRestrictionForChannelMeasurements parameter is configured for AI / ML-based temporal domain beam prediction, the parameter for defining the latest measurements (parameter M) will need to be redefined. The network device 120 may transmit the timeRestrictionForChannelMeasurements parameter to the terminal device 110 for configuring the time domain restriction for channel measurements.

[0057] In another example, the network device 120 may transmit, to the terminal device 110, the new higher layer parameter for configuring the number of time instances of the measurements in the observation window used by the terminal device, and the terminal device 110 is not expected to use the timeRestrictionForChannelMeasurements parameter. As such, the requirements of CSI-RS based Ll-RSRP reporting may be redefined based on the newly introduced parameter.

[0058] The terminal device 110 shall be capable of performing beam measurements, e.g., Ll-RSRP measurements, based on the configured CSI-RS resource for Ll-RSRP computation, and the UE physical layer shall be capable of reporting Ll-RSRP measured over the measurement period of Tu-RSrp Measurement period csi-rs-

[0059] As mentioned above, the measurements are reported based on measurement delay condition. In some embodiments, the measurement delay condition may comprise the Ll-RSRP measurement period, and the value of Ll-RSRP measurement period Tli-rsrp^Measurement j>ertod jisi -Rs may be determined based on a product of measurement parameters. These measurement parameters may comprise a first measurement parameter for defining latest measurements (parameter M), a second measurement parameter associated with measurement gap (parameter P), and a third measurement parameter associated with CSI-RS resources (parameter N).

[0060] By way of example without limitation, parameter M may represent the number of subframes or slots over which the Ll-RSRP measurements are averaged. P may be a parameter that determines if measurements gap should be considered or not in CSI report. If P=l, measurement gaps should not be considered, otherwise P should be determined based on measurements gaps. Furthermore, for periodic CSI-RS resources in a resource set configured with a higher layer parameter repetition set to OFF, N=l. For semi-persistent CSI-RS resources in a resource set configured with the higher layer parameter repetition set to OFF, N=l. The following table 1 show the determining process of measurement period TLl-RSRP_Measurement_Period_CSI-RS FR2.

[0061] Configuration ( BIS) non-DRX max( TReport, ceiRM * P * N) * TCSI_RS ) DRX cycle <320ms max( TReport, ceiR 1.5 * M * P * N) * max ( TDRX, TCSI_RS)) DRX cycle >320ms ceil(M * P * N) * TDRX Table 1. Measurement period TL1_RSRPMeasurement_Periodcsl_RS forFR2

[0062] Each of DRX configurations may correspond respective determining process of the measurement period TL1_RSRP Measurement-Period_csi—rs ■ In a case that the DRX configuration is non-discontinuous reception (non-DRX), the measurement period Tli-rsrp-Measurement_Period_csi-RS may be determined based on maximum one of ^Report or a product of TCS!_RS and the rounded-up result of M * P * N. TReport may indicate a configured periodicity for reporting measured Ll-RSRP. TCSI_RS may indicate a periodicity of CSI-RS configured for Ll-RSRP measurement. Furthermore, the parameter M is a parameter for defining latest measurements, the parameter Pisa parameter associated with measurement gap, and the parameter N is a parameter associated with CSI-RS resources.

[0063] In a case that the DRX configuration is DRX cycle <320ms, the measurement period TL1_RSRPMeasurementPeriod_csl_RS may be determined based on maximum one of TReport or a product of a rounded-up result of 1.5 * M * P * N and the maximum one of Tdrx andTCSI_RS. TReport may indicate a configured periodicity for reporting measured Ll-RSRP. Tcsi_rs may indicate a periodicity of CSI-RS configured for Ll-RSRP measurement, and TDRX may indicatethe DRX cycle length. Furthermore, the parameter M is a parameter for defining latest measurements, the parameter Pisa parameter associated with measurement gap, and the parameter N is a parameter associated with CSI-RS resources.

[0064] Moreover, in a case that the DRX configuration is DRX cycle >320ms, the measurement period TL1_RSRP Measurement-Period_csi-rs may be determined based on a product of Tdrx and a rounded-up result of M * P * N. TDRX may indicatethe DRX cycle length. Furthermore, the parameter M is a parameter for defining latest measurements, the parameter P is a parameter associated with measurement gap, and the parameter N is a parameter associated with CSI-RS resources. All these three implementations are provided that the CSI-RS resource configured for Ll-RSRP measurement is transmitted with Density =3.

[0065] In some embodiments, the first measurement parameter for defining the latest measurements (i.e., the parameter M with emphasis in Table 1) may be determined value(s) provided in the first higher layer parameter (i.e., the timeRestrictionForChannelMeasurements parameter) in a case that a parameter for Al / ML-based temporal domain beam prediction is configured for the terminal device.

[0066] For periodic and semi-persistent CSI-RS resources, when the UE is not configured with a higher layer parameter AIML BMCase2, M=1 if the higher layer parameter timeRestrictionForChannelMeasurement is configured, and M=3 otherwise if the higher layer parameter timeRestrictionForCharmelMeasurement is not configured. When the UE is configured with the higher layer parameter AIML BMCase2, M may be determined based on value(s) provided with the higher layer parameter timeRestrictionForChannelMeasiirement, where at least one value provides the length of the observation window.

[0067] In some embodiments, the first parameter for defining the latest measurements may be determined based on the second higher layer parameter(i.e., the newly introduced RRC parameter) for configuring the number of time instances of the measurements used by the terminal device in a case that the parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, where the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

[0068] For periodic and semi-persistent CSI-RS resources, when the UE is not configured with the higher layer parameter AIMLBMCase2, M=1 if the higher layer parameter timeRestrictionForChannelMeasurement is configured, and M=3 if the higher layer parameter timeRestrictionForChannelMeasurement is not configured. When the UE is configured with the higher layer parameter AIMI. BMCase2, M may be determined based on the new RRC higher layer parameter configured to the UE, which may indicate the number of time instances of the measurements in the observation window.

[0069] For aperiodic CSI-RS resources, if the UE is not configured with the higher layer parameter AIMI. BM('ase2, M=l, and if the UE is configured with the higher layer parameter AIMI. BMCase2, M may be determined based on the new RRC higher layer parameter configured to the UE, which may indicate the number of time instances of the measurements in the observation window.

[0070] In addition or alternatively, an additional parameter, a (hereinafter may also be referred to as sixth parameter), may be newly introduced, where the parameter a (with emphasis in the below Table 2) may indicate time offset (e.g., the parameter a may be set to satisfy the pre-processing time for AI / ML input). By way of example without limitation, the time-offset may represent the number of subframes or slots over which the Ll-RSRP measurements are on hold before being processed.

[0071] In one embodiment, the measurement parameters for the product for non-DRX may further comprise a parameter indicating a time offset a corresponding to pre-processing time for AI / ML input, i.e., M * a * P * N. In another embodiment, the parameter M may be configured with a time offset a indicated by a parameter, the time offset corresponds to preprocessing time for AI / ML input. The following table 2 show the determining process of measurement period TL1_RSRP^Measurement^Period^CSI_RS for FR2 with such time offset parameter.

[0072] Configuration rsrp Measurement_Period_CSI—RS (^S) non-DRX max( ^Report> ceil(M * a *P * N) * TCSI_RS ) or max( TReport, ceil((M + a) * P * N) * TCSI^ ) DRX cycle <320ms max( TReport, ceil( 1.5 * M * P * N) * max ( TDRX, TCS / ^RS)) DRX cycle >320ms ceil(M * P * N) * TDRX Table 2. Measurement period TL1_RSRPMeasurementPeriod_CSI_RS for FR2 with the time offset parameter

[0073] From the table 2, in a case that the DRX configuration is non-DRX, the measurement period RSRP_Measurement_Period_csi—rs may be determined based on maximum one of TReport or a product of TCSI_RS and the rounded-up result of M * a * P * N or (M + a) * P * N.

[0074] In addition or alternatively, an additional parameter, b (i.e., fifth parameter) may be introduced as length of observation window. The parameter b (with emphasis in the below Table 3) can be newly introduced for configuring the observation window for AI / ML BM-Case2.

[0075] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a second higher layer parameter b for configuring the number of time instances of the measurements used by the terminal device. The following table 3 show the determining process of measurement period 71i-RSRP^Measureznen^Period csi-rs for FR2 with such observation window parameter.

[0076] Configuration '^'Ll—RSRP_Measurement_Period_CSI—RS (^S) non-DRX max( TReport, ceil(M *b * P * N) * TCSI_RS ) DRX cycle <320ms max( TReport, ceil( 1.5 * M * P * N) * max ( TDRX, TCSI_RS)) DRX cycle >320ms ceil(M * P * N) * TDRX Table 3. Measurement period TL1_RSRPMeasurementPeriod_CSI_RS for FR2 with the length parameter

[0077] For periodic and semi-persistent CSI-RS resources, M=1 if the higher layer parameter timeRestrictionForChannelMeasurement is configured, and M=3 otherwise. For AI / ML BM-Case2, the parameter A length of observation window, if the higher layer parameter timeRestrictionForCharmelMeasurement is configured for the observation window (time domain of measured Ll-RSRP).

[0078] For AI / ML BM-Case2, the parameter ZMength of observation window, if the higher layer parameter timeRestrictionForCharmelMeasurement is not configured for the observation window, e.g., a newly introduced parameter is configured in RRC for length of the observation window. As such, the parameter b may indicate or configure the length of the observation window or the number of measurements that UE shall use regardless of whether the higher layer parameter timeRestrictionForChannelMeasurement is configured.

[0079] FIG. 3 illustrates a flowchart of an example method 300 implemented at a terminal device in accordance with some other embodiments of the present disclosure. It is to be understood that the method 300 may include additional blocks not shown and / or may omit some blocks as shown, and the scope of the present disclosure is not limited in this regard. For ease of understanding, the method 300 will be described from the perspective of the terminal device 110 with reference to FIG. 1.

[0080] At block 310, the terminal device 110 receives, from a network device 120, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition. At block 320, the terminal device 110 transmits, to the network device 120, a CSI report based on the CSI report configuration.

[0081] In some embodiments, the measurement delay condition may comprise a Ll-RSRP measurement period, the Ll-RSRP measurement period may be based on a product of measurement parameters comprising: a first measurement parameter for defining at least one latest measurement, a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSI-RS resources.

[0082] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a first higher layer parameter for configuring time domain restriction for channel measurements.

[0083] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

[0084] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for periodic and semi-persistent CSI-RS resources.

[0085] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[0086] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

[0087] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for (i) periodic and semi-persistent CSI-RS resources or (ii) aperiodic CSI-RS resources.

[0088] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input.

[0089] In some embodiments, the first measurement parameter for defining the at least one latest measurement for non-DRX may be configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

[0090] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[0091] In some embodiments, the terminal device may be further caused to receive an activation command for temporal domain beam prediction functionality. The activation command may be the CSI report configuration, wherein the CSI report configuration may further comprise time requirement defined with measurement period, delay related operations may be performed no later than the time requirement, and the delay related operations may comprise at least one of the following: the measurements in the observation window; use of AI / ML model for obtaining predictions; or report of the prediction.

[0092] In view of the above, some embodiments of the disclosure provide a signaling framework to enable AI / ML-based temporal domain beam prediction and to indicate a number of time instances for measurements and report the measurements based on the measurement delay condition.

[0093] FIG. 4 illustrates a flowchart of an example method 400 implemented at a network device in accordance with some other embodiments of the present disclosure. It is to be understood that the method 400 may include additional blocks not shown and / or may omit some blocks as shown, and the scope of the present disclosure is not limited in this regard. For ease of understanding, the method 400 will be described from the perspective of the terminal device 110 with reference to FIG. 1.

[0094] At block 410, the network device 120 transmits, to a terminal device a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition. At block 420, the network device 110 receives, from the terminal devicel 10, a CSI report based on the CSI report configuration.

[0095] In some embodiments, the measurement delay condition may comprise a Ll-RSRP measurement period, the Ll-RSRP measurement period may be based on a product of measurement parameters comprising: a first measurement parameter for defining at least one latest measurement, a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSI-RS resources.

[0096] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a first higher layer parameter for configuring time domain restriction for channel measurements.

[0097] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

[0098] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for periodic and semi-persistent CSI-RS resources.

[0099] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00100] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

[00101] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for (i) periodic and semi-persistent CSI-RS resources or (ii) aperiodic CSI-RS resources.

[00102] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input.

[00103] In some embodiments, the first measurement parameter for defining the at least one latest measurement for non-DRX may be configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

[00104] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00105] In some embodiments, an activation command for temporal domain beam prediction functionality may be received by the UE. The activation command may be the CSI report configuration, wherein the CSI report configuration may further comprise time requirement defined with measurement period, delay related operations may be performed no later than the time requirement, and the delay related operations may comprise at least one of the following: the measurements in the observation window; use of AI / ML model for obtaining predictions; or report of the prediction.

[00106] In some embodiments, an apparatus capable of performing the method 300 (for example, the terminal device 110) may comprise means for performing the respective steps of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

[00107] In some example embodiments, the apparatus comprises: means for receiving, from a network device 120, a CSI report configuration for AI / ML-based BM for temporal domain 20 beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and means for transmitting, to the network device 120, a CSI report based on the CSI report configuration.

[00108] In some embodiments, the measurement delay condition may comprise a Ll-RSRP measurement period, the Ll-RSRP measurement period may be based on a product of measurement parameters comprising: a first measurement parameter for defining at least one latest measurement, a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSLRS resources.

[00109] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a first higher layer parameter for configuring time domain restriction for channel measurements.

[00110] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

[00111] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for periodic and semi-persistent CSI-RS resources.

[00112] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00113] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

[00114] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for (i) periodic and semi-persistent CSLRS resources or (ii) aperiodic CSI-RS resources.

[00115] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input.

[00116] In some embodiments, the first measurement parameter for defining the at least one latest measurement for non-DRX may be configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

[00117] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00118] In some embodiments, the apparatus may further comprise: means for receiving an activation command for temporal domain beam prediction functionality. The activation command may be the CSI report configuration, wherein the CSI report configuration may further comprise time requirement defined with measurement period, delay related operations may be performed no later than the time requirement, and the delay related operations may comprise at least one of the following: the measurements in the observation window; use of AI / ML model for obtaining predictions; or report of the prediction.

[00119] In some embodiments, an apparatus capable of performing the method 400 (for example, the network device 120) may comprise means for performing the respective steps of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. In some example embodiments, the apparatus comprises: means for transmitting, to a terminal device, a CSI report configuration for AI / ML-based BM for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; and means for receiving, from the terminal device, a CSI report based on the CSI report configuration

[00120] In some embodiments, the measurement delay condition may comprise a Ll-RSRP measurement period, the Ll-RSRP measurement period may be based on a product of measurement parameters comprising: a first measurement parameter for defining at least one latest measurement,|a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSI-RS resources.

[00121] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a first higher layer parameter for configuring time domain restriction for channel measurements.

[00122] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

[00123] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for periodic and semi-persistent CSI-RS resources.

[00124] In some embodiments, the parameter indicating the number of time instances for the measurements in the observation window may comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00125] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

[00126] In some embodiments, the first measurement parameter for defining the at least one latest measurement may be configured for (i) periodic and semi-persistent CSI-RS resources or (ii) aperiodic CSI-RS resources.

[00127] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input.

[00128] In some embodiments, the first measurement parameter for defining the at least one latest measurement for non-DRX may be configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

[00129] In some embodiments, the measurement parameters for the product for non-DRX may further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

[00130] In some embodiments, an activation command for temporal domain beam prediction functionality may be received by the UE. The activation command may be the CSI report configuration, wherein the CSI report configuration may further comprise time requirement defined with measurement period, delay related operations may be performed no later than the time requirement, and the delay related operations may comprise at least one of the following: the measurements in the observation window; use of AVML model for obtaining predictions; or report of the prediction.

[00131] FIG. 5 illustrates a simplified block diagram of a device 500 that is suitable for implementing some example embodiments of the present disclosure. The device 500 may be provided to implement a communication device, for example, the terminal device 110 or the network device 120 as shown in FIG. 1. As shown, the device 500 includes one or more processors 510, one or more memories 520 coupled to the processor 510, and one or more communication modules 540 coupled to the processor 510.

[00132] The communication module 540 is for bidirectional communications. The communication module 540 has at least one antenna to facilitate communication. The communication interface may represent any interface that is necessary for communication with other network elements.

[00133] The processor 510 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 500 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[00134] The memory 520 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 524, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 522 and other volatile memories that will not last in the power-down duration.

[00135] A computer program 530 includes computer executable instructions that are executed by the associated processor 510. The program 530 may be stored in the ROM 524. The processor 510 may perform any suitable actions and processing by loading the program 530 into the RAM 522.

[00136] The embodiments of the present disclosure may be implemented by means of the program 530 so that the device 500 may perform any process of the disclosure as discussed with reference to FIGS. 3 and 4. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[00137] In some example embodiments, the program 530 may be tangibly contained in a computer-readable medium which may be included in the device 500 (such as in the memory 520) or other storage devices that are accessible by the device 500. The device 500 may load the program 530 from the computer-readable medium to the RAM 522 for execution. The computer-readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like.

[00138] FIG. 6 illustrates a block diagram of an example of a computer-readable medium 600 in accordance with some example embodiments of the present disclosure. The computer-readable medium 600 has the program 530 stored thereon. It is noted that although the computer-readable medium 600 is depicted in form of CD or DVD in FIG. 6, the computer-readable medium 600 may be in any other form suitable for carry or hold the program 530.

[00139] Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[00140] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the method 300 or 400 as described above with reference to FIG. 3 or 4. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[00141] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[00142] In the context of the present disclosure, the computer program codes or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer-readable medium, and the like.

[00143] The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e g., RAM vs. ROM).

[00144] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular 5 embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.

[00145] Although the present disclosure has been described in languages specific to 10 structural features and / or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A terminal device comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to:receive a channel state information, CSI, report configuration for artificial intelligence / machine learning, AI / ML-based beam management, BM, for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; andtransmit a CSI report based on the CSI report configuration.

2. The terminal device of claim 1, wherein the measurement delay condition comprises a layer 1-reference signal received power, Ll-RSRP, measurement period, the LI -RSRP measurement period is based on a product of measurement parameters comprising:a first measurement parameter for defining at least one latest measurement, a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSI-RS resources.

3. The terminal device of claim 1 or 2, wherein the parameter indicating the number of time instances for the measurements in the observation window comprises a first higher layer parameter for configuring time domain restriction for channel measurements.

4. The terminal device of claim 3, wherein the first measurement parameter for defining the at least one latest measurement is based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

5. The terminal device of claim 4, wherein the first measurement parameter for defining the at least one latest measurement is configured for periodic and semi-persistent CSI-RS resources.

6. The terminal device of claim 1 or 2, wherein the parameter indicating the number of time instances for the measurements in the observation window comprises a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

7. The terminal device of claim 6, wherein the first measurement parameter for defining the at least one latest measurement is based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

8. The terminal device of claim 6, wherein the first measurement parameter for defining the at least one latest measurement is configured for (i) periodic and semi-persistent CSI-RS resources or (ii) aperiodic CSI-RS resources.

9. The terminal device of any of claims 2-8, wherein the measurement parameters for the product for, non-discontinuous reception, non-DRX, further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input.

10. The terminal device of any of claims 2-8, wherein the first measurement parameter for defining the at least one latest measurement for non-DRX is configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

11. The terminal device of any of claims 2-10, wherein the measurement parameters for the product for non-DRX further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

12. The terminal device of any of claims 2-11, wherein the terminal device is further caused to receive an activation command for temporal domain beam prediction functionality, wherein the activation command is the CSI report configuration, wherein the CSI report configuration further comprises time requirement defined with measurement period, delayrelated operations are performed no later than the time requirement, and the delay related operations comprise at least one of the following:the measurements in the observation window;use of AI / ML model for obtaining predictions; orreport of the prediction.

13. A network device comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to:transmit, to a terminal device, a channel state information, CS1, report configuration for artificial intelligence / machine learning, AI / ML-based beam management, BM, for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; andreceive, from the terminal device, a CSI report based on the CSI report configuration.

14. The network device of claim 13, wherein the measurement delay condition comprises a layer 1-reference signal received power, Ll-RSRP, measurement period, the LI -RSRP measurement period is based on a product of measurement parameters comprising:a first measurement parameter for defining at least one latest measurement, a second measurement parameter associated with measurement gap, and a third measurement parameter associated with CSI-RS resources.

15. The network device of claim 13 or 14, wherein the parameter indicating the number of time instances for the measurements in the observation window comprises a first higher layer parameter for configuring time domain restriction for channel measurements.

16. The network device of claim 15, wherein the first measurement parameter for defining the at least one latest measurement is based on at least one value provided in the first higher layer parameter in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device.

17. The network device of claim 15, wherein the first measurement parameter for defining the at least one latest measurement is configured for periodic and semi-persistent CSI-RS resources.

18. The network device of claim 13 or 14, wherein the parameter indicating the number of time instances for the measurements in the observation window comprises a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.

19. The network device of claim 18, wherein the first measurement parameter for defining the at least one latest measurement is based on the second higher layer parameter configured to the terminal device in a case that a parameter for AI / ML-based temporal domain beam prediction is configured for the terminal device, wherein the second higher layer parameter indicates the number of time instances of the measurements in the observation window.

20. The network device of claim 18, wherein the first measurement parameter for defining the at least one latest measurement is configured for (i) periodic and semi-persistent CSI-RS resources or (ii) aperiodic CSI-RS resources.

21. The network device of any of claims 14-20,wherein the measurement parameters for the product for, non-discontinuous reception, non-DRX, further comprise a parameter indicating a time offset corresponding to pre-processing time for AI / ML input; and / orwherein the first measurement parameter for defining the at least one latest measurement for non-DRX is configured with a time offset indicated by a parameter, the time offset corresponds to pre-processing time for AI / ML input.

22. The network device of any of claims 14-21, wherein the measurement parameters for the product for non-DRX further comprise a second higher layer parameter for configuring the number of time instances of the measurements used by the terminal device.23 The network device of any of claims 14-22, wherein an activation command fortemporal domain beam prediction functionality is received by the terminal device, whereinthe activation command is the CSI report configuration, wherein the CSI report configuration further comprises time requirement defined with measurement period, delay related operations are performed no later than the time requirement, and the delay related operations comprise at least one of the following:the measurements in the observation window;use of Al / ML model for obtaining predictions; orreport of the prediction.

24. A method comprising:receiving, from a network device, a channel state information, CSI, report configuration for artificial intelligence / machine learning, Al / ML-based beam management, BM, for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; andtransmitting, to the network device, a CSI report based on the CSI report configuration.

25. A method comprising:transmitting, to a terminal device, a channel state information, CSI, report configuration for artificial intelligence / machine learning, AI / ML-based beam management, BM, for temporal domain beam prediction, including a parameter indicating a number of time instances for measurements in an observation window and reporting the measurements based on measurement delay condition; andreceiving, from the terminal device, a CSI report based on the CSI report configuration.

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